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3D reconstruction of building information models with weakly-supervised learning for carbon emission modelling in the built environment

Author

Listed:
  • Gan, Vincent J.L.
  • Li, Kexin
  • Li, Mingkai
  • Halfian, Leanna B.E.

Abstract

Buildings and construction activities contribute around 38.0 % of global CO2 emissions, emphasising the importance of carbon emission modelling in assessing the built environment's carbon footprint. For accurate carbon emission modelling, detailed as-built information on building structures is crucial, which is often challenged by uncertainties due to design modifications and construction variations. Scan-to-BIM technology mitigates this problem by capturing precise as-built geometries through point clouds and transforming them into detailed 3D digital models. However, traditional 3D modelling methods often rely on manual intervention in creating as-built BIM models, which leads to low accuracy and efficiency. This paper presents an AI-enhanced approach that employs weakly-supervised learning for automated BIM reconstruction, aiming at accurate carbon performance evaluation in the built environment. By employing weakly-supervised semantic segmentation, this approach segments structural components from 3D point clouds and formulates the topological relationships of building objects, which enhances the automation of BIM reconstruction. This ensures a detailed representation of dimensions and materials, facilitating effective carbon emission modelling. The results reveal marked improvements in both semantic segmentation and BIM model accuracy. Specifically, the WSSIS network demonstrates an OA of 99.42 % and a mIoU of 98.27 % for semantic segmentation. Moreover, over 80.0 % of the point clouds meet the 5.0 mm tolerance requirements for BIM model accuracy. These BIM models are then used to assess the upfront carbon footprint of construction materials, as well as to model carbon emissions from usage to demolition. This method significantly enhances accuracy in evaluating and understanding the carbon impact within the built environment, and represents a noteworthy leap towards eco-friendly architectural and construction practices that aim to reduce carbon emissions.

Suggested Citation

  • Gan, Vincent J.L. & Li, Kexin & Li, Mingkai & Halfian, Leanna B.E., 2025. "3D reconstruction of building information models with weakly-supervised learning for carbon emission modelling in the built environment," Applied Energy, Elsevier, vol. 377(PD).
    • Handle: RePEc:eee:appene:v:377:y:2025:i:pd:s0306261924020786
    DOI: 10.1016/j.apenergy.2024.124695
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    References listed on IDEAS

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    1. Röck, Martin & Saade, Marcella Ruschi Mendes & Balouktsi, Maria & Rasmussen, Freja Nygaard & Birgisdottir, Harpa & Frischknecht, Rolf & Habert, Guillaume & Lützkendorf, Thomas & Passer, Alexander, 2020. "Embodied GHG emissions of buildings – The hidden challenge for effective climate change mitigation," Applied Energy, Elsevier, vol. 258(C).
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